Autonomous Vehicles in Poland
A Latent-Structure Analysis of Technology Perception Based on Survey Data and Focus Group Validation
Bibliographic Data
| ID | 19489248 |
|---|---|
| Authors | Maciej Kozłowski (0000-0002-1068-8991, Warsaw University of Technology), Andrzej Czerepicki (0000-0002-8659-5695, Warsaw University of Technology, corresponding author) |
| Year | 2026 |
| Volume | 10 |
| Issue | 5 |
| Pages | 243 |
| Publication date | 2026-04-30 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Urban Science (JOURNAL) |
| Journal identifiers | ISSN: 2413-8851 • E-ISSN: 2413-8851 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/urbansci10050243 |
| OpenAlex | W7160018688 |
| Language | EN |
| References cited | 23 |
This article draws on public opinion surveys conducted as part of the AV-PL-ROAD project, “Polish Road to Automation of Road Transport”. Although selected findings from this survey material were published in 2023, the earlier study was limited to descriptive statistical analysis. The present paper re-examines the same empirical dataset through a different analytical framework focused on latent-structure reconstruction, using a different analytical framework focused on latent-structure reconstruction, providing a more structured and informative interpretation of perceptions of autonomous vehicles in Poland. The study combines within-respondent standardization, Principal Component Analysis (PCA), and k-means clustering to identify the dominant dimensions of perception and recurring perception profiles, complemented by qualitative insights from focus group interviews (FGI) used to support interpretation. The results indicate that perceptions of autonomous vehicles are not one-dimensional, but are organized around three main axes: systemic benefits versus implementation barriers, technological trust and information security, and regulatory-ethical readiness linked to deployment conditions. The analysis also reveals four recurring perception profiles that do not map directly onto simple demographic divisions and are better understood in relation to operational and institutional context. In addition, statistically significant differences between clusters were confirmed using nonparametric tests (Kruskal–Wallis with Dunn–Šidák post hoc analysis). The main contribution of the paper is methodological: it illustrates that previously analyzed survey data can yield structurally informative insights, including the identification of latent dimensions, perception profiles, and statistically significant differences between clusters when reinterpreted through a latent-space approach rather than conventional descriptive methods. The findings provide additional evidence on the social and institutional conditions of transport automation in Poland and provide a more robust analytical basis for future mobility policy and implementation strategies
Cluster analysis · Correspondence analysis · Descriptive statistics · Focus group · Nonparametric statistics · Perception · Software deployment · Survey data collection · Autonomous Vehicle Technology and Safety · Human-Automation Interaction and Safety · Transportation and Mobility Innovations
| Highly cited | No |
|---|